{"ID":2886217,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.03216","arxiv_id":"2508.03216","title":"Navigation Pixie: Implementation and Empirical Study Toward On-demand Navigation Agents in Commercial Metaverse","abstract":"While commercial metaverse platforms offer diverse user-generated content, they lack effective navigation assistance that can dynamically adapt to users' interests and intentions. Although previous research has investigated on-demand agents in controlled environments, implementation in commercial settings with diverse world configurations and platform constraints remains challenging. We present Navigation Pixie, an on-demand navigation agent employing a loosely coupled architecture that integrates structured spatial metadata with LLM-based natural language processing while minimizing platform dependencies, which enables experiments on the extensive user base of commercial metaverse platforms. Our cross-platform experiments on commercial metaverse platform Cluster with 99 PC client and 94 VR-HMD participants demonstrated that Navigation Pixie significantly increased dwell time and free exploration compared to fixed-route and no-agent conditions across both platforms. Subjective evaluations revealed consistent on-demand preferences in PC environments versus context-dependent social perception advantages in VR-HMD. This research contributes to advancing VR interaction design through conversational spatial navigation agents, establishes cross-platform evaluation methodologies revealing environment-dependent effectiveness, and demonstrates empirical experimentation frameworks for commercial metaverse platforms.","short_abstract":"While commercial metaverse platforms offer diverse user-generated content, they lack effective navigation assistance that can dynamically adapt to users' interests and intentions. Although previous research has investigated on-demand agents in controlled environments, implementation in commercial settings with diverse...","url_abs":"https://arxiv.org/abs/2508.03216","url_pdf":"https://arxiv.org/pdf/2508.03216v1","authors":"[\"Hikari Yanagawa\",\"Yuichi Hiroi\",\"Satomi Tokida\",\"Yuji Hatada\",\"Takefumi Hiraki\"]","published":"2025-08-05T08:45:34Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"LoRA\"]","has_code":false}